Finding Longitude: How Ships, Clocks and Stars helped solve the Longitude Problem
Bibliographic record
Abstract
A tale of eighteenth-century invention and competition, commerce and conflict, this is a lively, illustrated, and accurate chronicle of the search to solve “the longitude problem,” the question of how to determine a ship’s position at sea—and one that changed the history of mankind. Ships, Clocks, and Stars brings into focus one of our greatest scientific stories: the search to accurately measure a ship’s position at sea. The incredible, illustrated volume reveals why longitude mattered to seafaring nations, illuminates the various solutions that were proposed and tested, and explores the invention that revolutionized human history and the man behind it, John Harrison. Here, too, are the voyages of Captain Cook that put these revolutionary navigational methods to the test. Filled with astronomers, inventors, politicians, seamen, and satirists, Ships, Clocks, and Stars explores the scientific, political, and commercial battles of the age, as well as the sailors, ships, and voyages that made it legend—from Matthew Flinders and George Vancouver to the voyages of the Bounty and the Beagle. Featuring more than 150 photographs specially commissioned from Britain’s National Maritime Museum, this evocative, detailed, and thoroughly fascinating history brings this age of exploration and enlightenment vividly to life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".